LitLogger

To use LitLogger <https://lightning.ai/docs/overview/experiment-management>_ first install the litlogger package:

.. code-block:: bash

pip install litlogger

Configure the logger and pass it to the :class:~lightning.pytorch.trainer.trainer.Trainer:

.. code-block:: python

from lightning.pytorch.loggers import LitLogger

lit_logger = LitLogger(save_dir="logs/")
trainer = Trainer(logger=lit_logger)

Access the litlogger logger from any function (except the LightningModule init) to use its API for tracking advanced artifacts

.. code-block:: python

class LitModel(LightningModule):
    def any_lightning_module_function_or_hook(self):
        lit_logger = self.logger.experiment
        lit_logger.log_file("generated_images.txt")

Here's the full documentation for the :class:~lightning.pytorch.loggers.LitLogger.

Comet.ml

To use Comet.ml <https://www.comet.ml/site/>_ first install the comet package:

.. code-block:: bash

pip install comet-ml

Configure the logger and pass it to the :class:~lightning.pytorch.trainer.trainer.Trainer:

.. code-block:: python

from lightning.pytorch.loggers import CometLogger

comet_logger = CometLogger(api_key="YOUR_COMET_API_KEY")
trainer = Trainer(logger=comet_logger)

Access the comet logger from any function (except the LightningModule init) to use its API for tracking advanced artifacts

.. code-block:: python

class LitModel(LightningModule):
    def any_lightning_module_function_or_hook(self):
        comet = self.logger.experiment
        fake_images = torch.Tensor(32, 3, 28, 28)
        comet.add_image("generated_images", fake_images, 0)

Here's the full documentation for the :class:~lightning.pytorch.loggers.CometLogger.

MLflow

To use MLflow <https://mlflow.org/>_ first install the MLflow package:

.. code-block:: bash

pip install mlflow

Configure the logger and pass it to the :class:~lightning.pytorch.trainer.trainer.Trainer:

.. code-block:: python

from lightning.pytorch.loggers import MLFlowLogger

mlf_logger = MLFlowLogger(experiment_name="lightning_logs", tracking_uri="file:./ml-runs")
trainer = Trainer(logger=mlf_logger)

Access the mlflow logger from any function (except the LightningModule init) to use its API for tracking advanced artifacts

.. code-block:: python

class LitModel(LightningModule):
    def any_lightning_module_function_or_hook(self):
        mlf_logger = self.logger.experiment
        fake_images = torch.Tensor(32, 3, 28, 28)
        mlf_logger.add_image("generated_images", fake_images, 0)

Here's the full documentation for the :class:~lightning.pytorch.loggers.MLFlowLogger.

Neptune.ai

To use Neptune.ai <https://www.neptune.ai/>_ first install the neptune package:

.. code-block:: bash

pip install neptune

or with conda:

.. code-block:: bash

conda install -c conda-forge neptune

Configure the logger and pass it to the :class:~lightning.pytorch.trainer.trainer.Trainer:

.. testcode:: :skipif: not _NEPTUNE_AVAILABLE

import neptune
from lightning.pytorch.loggers import NeptuneLogger

neptune_logger = NeptuneLogger(
    api_key=neptune.ANONYMOUS_API_TOKEN,  # replace with your own
    project="common/pytorch-lightning-integration",  # format "<WORKSPACE/PROJECT>"
)
trainer = Trainer(logger=neptune_logger)

Access the neptune logger from any function (except the LightningModule init) to use its API for tracking advanced artifacts

.. code-block:: python

class LitModel(LightningModule):
    def any_lightning_module_function_or_hook(self):
        neptune_logger = self.logger.experiment["your/metadata/structure"]
        neptune_logger.append(metadata)

Here's the full documentation for the :class:~lightning.pytorch.loggers.NeptuneLogger.

Tensorboard

TensorBoard <https://pytorch.org/docs/stable/tensorboard.html>_ can be installed with:

.. code-block:: bash

pip install tensorboard

Configure the logger and pass it to the :class:~lightning.pytorch.trainer.trainer.Trainer:

.. code-block:: python

from lightning.pytorch.loggers import TensorBoardLogger

logger = TensorBoardLogger()
trainer = Trainer(logger=logger)

Access the tensorboard logger from any function (except the LightningModule init) to use its API for tracking advanced artifacts

.. code-block:: python

class LitModel(LightningModule):
    def any_lightning_module_function_or_hook(self):
        tensorboard_logger = self.logger.experiment
        fake_images = torch.Tensor(32, 3, 28, 28)
        tensorboard_logger.add_image("generated_images", fake_images, 0)

Here's the full documentation for the :class:~lightning.pytorch.loggers.TensorBoardLogger.

Weights and Biases

To use Weights and Biases <https://docs.wandb.ai/guides/integrations/lightning>_ (wandb) first install the wandb package:

.. code-block:: bash

pip install wandb

Configure the logger and pass it to the :class:~lightning.pytorch.trainer.trainer.Trainer:

.. testcode:: :skipif: not _WANDB_AVAILABLE

from lightning.pytorch.loggers import WandbLogger

wandb_logger = WandbLogger(project="MNIST", log_model="all")
trainer = Trainer(logger=wandb_logger)

# log gradients and model topology
wandb_logger.watch(model)

Access the wandb logger from any function (except the LightningModule init) to use its API for tracking advanced artifacts

.. code-block:: python

class MyModule(LightningModule):
    def any_lightning_module_function_or_hook(self):
        wandb_logger = self.logger.experiment
        fake_images = torch.Tensor(32, 3, 28, 28)

        # Option 1
        wandb_logger.log({"generated_images": [wandb.Image(fake_images, caption="...")]})

        # Option 2 for specifically logging images
        wandb_logger.log_image(key="generated_images", images=[fake_images])

Here's the full documentation for the :class:~lightning.pytorch.loggers.WandbLogger. Demo in Google Colab <http://wandb.me/lightning>__ with hyperparameter search and model logging.

Use multiple exp managers

To use multiple experiment managers at the same time, pass a list to the logger :class:~lightning.pytorch.trainer.trainer.Trainer argument.

.. testcode:: :skipif: (not _TENSORBOARD_AVAILABLE and not _TENSORBOARDX_AVAILABLE) or not _WANDB_AVAILABLE

from lightning.pytorch.loggers import TensorBoardLogger, WandbLogger

logger1 = TensorBoardLogger()
logger2 = WandbLogger()
trainer = Trainer(logger=[logger1, logger2])

Access all loggers from any function (except the LightningModule init) to use their APIs for tracking advanced artifacts

.. code-block:: python

class MyModule(LightningModule):
    def any_lightning_module_function_or_hook(self):
        tensorboard_logger = self.loggers.experiment[0]
        wandb_logger = self.loggers.experiment[1]

        fake_images = torch.Tensor(32, 3, 28, 28)

        tensorboard_logger.add_image("generated_images", fake_images, 0)
        wandb_logger.add_image("generated_images", fake_images, 0)